Effects of visualization and note-taking on sensemaking and analysis

Goyal, Nitesh; Leshed, Gilly; Fussell, Susan R.

doi:10.1145/2470654.2481376

Cited by 29 publications

(17 citation statements)

References 13 publications

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“…The key clue to naming the killer was included in one of the witness reports of the active case, although the active case was not one of the serial killer cases. The task for this study was carefully designed to include data and aspects that are similar to real-world crime cases that remain unsolved, at a scale that could be analyzed in a one-hour session, and at a level of difficulty where many people are unable to solve the crime [1,17].…”

Section: Methodsmentioning

confidence: 99%

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Effects of implicit sharing in collaborative analysis

Goyal

Leshed

Cosley

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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When crime analysts collaborate to solve crime cases, they need to share insights in order to connect the clues, identify a pattern, and attribute the crime to the right culprit. We designed a collaborative analysis tool to explore the value of implicitly sharing insights and notes, without requiring analysts to explicitly push information or request it from each other. In an experiment, pairs of remote individuals played the role of crime analysts solving a set of serial killer crimes with both partners having some, but not all, relevant clues. When implicit sharing of notes was available, participants remembered more clues related to detecting the serial killer, and they perceived the tool as more useful compared to when implicit sharing was not available.

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Section: Methodsmentioning

confidence: 99%

“…We adapted Goyal et al's SAVANT system [17] for use in the current study. SAVANT has two main components, the Document Space ( Figure 1) and the Analysis Space ( Figure 2).…”

Section: Research Prototype Toolmentioning

confidence: 99%

Effects of implicit sharing in collaborative analysis

Goyal

Leshed

Cosley

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…For example, when detectives group crimes according to the weapon used, their teammates may more readily detect a pattern, if one is present. Along related lines, visualization tools that display otherwise hidden linkages across dispersed information have improved complex problem solving [2,10,13,33].…”

Section: Organizing Categorizing and Creating Schemasmentioning

confidence: 99%

“…Annotation tools, for example, enable analysts to highlight specific pieces of evidence for one another [16]. Organizing and visualizing tools enable analysts to display linkages across dispersed pieces of evidence, helping them point out patterns [2,10,13]. Researchers have not yet examined how the quality of the information conveyed through different tools affects collaborative analysis.…”

Section: Introductionmentioning

confidence: 99%

Teammate inaccuracy blindness

Kang

Kane

Kiesler

2014

Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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Asynchronous collaborative analysis is important in many fields, but information sharing can be a bottleneck. Tools for annotating, organizing, and summarizing information can help, but their value will likely depend on the accuracy of teammates' information. To document this claim, two experiments examined participants' performance on a complex detective task when they asynchronously received information from a teammate in a collaboration tool or received no such information. We found that receiving a progress report containing accurate information was associated with improved performance (vs. no information shared in a tool) but worse performance when the information was inaccurate. Teammates were evaluated as helpful even when they were not. Our findings point to a phenomenon of teammate inaccuracy blindness that arises when teammates provide inaccurate information. We propose some strategies for helping collaborators avoid or lessen this effect.

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“…We examine the effects of our sensemaking translucence interface in a laboratory study in which pairs of remote participants role-played detectives collaborating to solve a serial killer crime [2]. Half of the pairs used the sensemaking translucence interface and the other half used a standard interface [22] used in previous studies [19,21]. As we will show, pairs using the sensemaking translucence interface were significantly better than those using the standard interface at uncovering pertinent clues and identifying the serial killer.…”

Section: Introductionmentioning

confidence: 99%

Effects of Sensemaking Translucence on Distributed Collaborative Analysis

Goyal

Fussell

2016

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &Amp; Social Computing

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Collaborative sensemaking requires that analysts share their information and insights with each other, but this process of sharing runs the risks of prematurely focusing the investigation on specific suspects. To address this tension, we propose and test an interface for collaborative crime analysis that aims to make analysts more aware of their sensemaking processes. We compare our sensemaking translucence interface to a standard interface without special sensemaking features in a controlled laboratory study. We found that the sensemaking translucence interface significantly improved clue finding and crime solving performance, but that analysts rated the interface lower on subjective measures than the standard interface. We conclude that designing for distributed sensemaking requires balancing task performance vs. user experience and real-time information sharing vs. data accuracy.

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Effects of visualization and note-taking on sensemaking and analysis

Cited by 29 publications

References 13 publications

Effects of implicit sharing in collaborative analysis

Effects of implicit sharing in collaborative analysis

Teammate inaccuracy blindness

Effects of Sensemaking Translucence on Distributed Collaborative Analysis

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